Overview

Dataset statistics

Number of variables17
Number of observations130663
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory42.6 MiB
Average record size in memory342.0 B

Variable types

Text3
Numeric12
Categorical2

Alerts

acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
energy is highly overall correlated with acousticness and 1 other fieldsHigh correlation
loudness is highly overall correlated with acousticness and 1 other fieldsHigh correlation
time_signature is highly imbalanced (67.8%)Imbalance
instrumentalness has 42133 (32.2%) zerosZeros
key has 14972 (11.5%) zerosZeros
popularity has 18889 (14.5%) zerosZeros

Reproduction

Analysis started2023-06-28 14:04:08.496321
Analysis finished2023-06-28 14:04:42.116703
Duration33.62 seconds
Software versionydata-profiling vv4.3.1
Download configurationconfig.json

Variables

Distinct34621
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size8.7 MiB
2023-06-28T16:04:42.439125image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length75
Median length50
Mean length11.741266
Min length1

Characters and Unicode

Total characters1534149
Distinct characters579
Distinct categories18 ?
Distinct scripts19 ?
Distinct blocks26 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19495 ?
Unique (%)14.9%

Sample

1st rowYG
2nd rowYG
3rd rowR3HAB
4th rowChris Cooq
5th rowChris Cooq
ValueCountFrequency (%)
the 5160
 
2.0%
sebastian 3690
 
1.4%
johann 3639
 
1.4%
bach 3631
 
1.4%
2256
 
0.9%
van 2232
 
0.9%
wolfgang 1764
 
0.7%
mozart 1758
 
0.7%
amadeus 1755
 
0.7%
los 1440
 
0.6%
Other values (31641) 229175
89.3%
2023-06-28T16:04:42.993188image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 134241
 
8.8%
e 126440
 
8.2%
125881
 
8.2%
n 96332
 
6.3%
o 89826
 
5.9%
i 86485
 
5.6%
r 75326
 
4.9%
s 60058
 
3.9%
l 58981
 
3.8%
t 52812
 
3.4%
Other values (569) 627767
40.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1102646
71.9%
Uppercase Letter 289149
 
18.8%
Space Separator 125883
 
8.2%
Other Punctuation 8172
 
0.5%
Decimal Number 4872
 
0.3%
Dash Punctuation 1639
 
0.1%
Currency Symbol 647
 
< 0.1%
Other Letter 466
 
< 0.1%
Close Punctuation 152
 
< 0.1%
Open Punctuation 150
 
< 0.1%
Other values (8) 373
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10
 
2.1%
8
 
1.7%
6
 
1.3%
6
 
1.3%
6
 
1.3%
6
 
1.3%
5
 
1.1%
5
 
1.1%
4
 
0.9%
4
 
0.9%
Other values (285) 406
87.1%
Lowercase Letter
ValueCountFrequency (%)
a 134241
12.2%
e 126440
11.5%
n 96332
 
8.7%
o 89826
 
8.1%
i 86485
 
7.8%
r 75326
 
6.8%
s 60058
 
5.4%
l 58981
 
5.3%
t 52812
 
4.8%
h 43888
 
4.0%
Other values (97) 278257
25.2%
Uppercase Letter
ValueCountFrequency (%)
S 28185
 
9.7%
B 23022
 
8.0%
M 20669
 
7.1%
A 19358
 
6.7%
C 17063
 
5.9%
T 16806
 
5.8%
L 15891
 
5.5%
D 15820
 
5.5%
J 14950
 
5.2%
R 13329
 
4.6%
Other values (79) 104056
36.0%
Other Punctuation
ValueCountFrequency (%)
. 4167
51.0%
& 2042
25.0%
' 945
 
11.6%
! 317
 
3.9%
, 287
 
3.5%
" 120
 
1.5%
/ 114
 
1.4%
: 93
 
1.1%
? 30
 
0.4%
* 14
 
0.2%
Other values (11) 43
 
0.5%
Other Symbol
ValueCountFrequency (%)
12
25.0%
8
16.7%
7
14.6%
° 5
10.4%
3
 
6.2%
2
 
4.2%
2
 
4.2%
2
 
4.2%
© 1
 
2.1%
1
 
2.1%
Other values (5) 5
10.4%
Nonspacing Mark
ValueCountFrequency (%)
2
14.3%
1
 
7.1%
̛ 1
 
7.1%
1
 
7.1%
1
 
7.1%
ؖ 1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
1
 
7.1%
Other values (3) 3
21.4%
Decimal Number
ValueCountFrequency (%)
3 773
15.9%
0 670
13.8%
1 616
12.6%
2 595
12.2%
4 422
8.7%
8 389
8.0%
9 386
7.9%
5 367
7.5%
6 330
6.8%
7 324
6.7%
Math Symbol
ValueCountFrequency (%)
+ 74
77.9%
> 11
 
11.6%
| 3
 
3.2%
× 2
 
2.1%
= 2
 
2.1%
~ 1
 
1.1%
1
 
1.1%
< 1
 
1.1%
Currency Symbol
ValueCountFrequency (%)
$ 643
99.4%
£ 3
 
0.5%
¥ 1
 
0.2%
Close Punctuation
ValueCountFrequency (%)
) 101
66.4%
] 49
32.2%
2
 
1.3%
Final Punctuation
ValueCountFrequency (%)
65
94.2%
3
 
4.3%
» 1
 
1.4%
Modifier Symbol
ValueCountFrequency (%)
^ 38
90.5%
´ 3
 
7.1%
` 1
 
2.4%
Space Separator
ValueCountFrequency (%)
125881
> 99.9%
  2
 
< 0.1%
Open Punctuation
ValueCountFrequency (%)
( 101
67.3%
[ 49
32.7%
Initial Punctuation
ValueCountFrequency (%)
3
75.0%
« 1
 
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 1639
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 92
100.0%
Modifier Letter
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1388865
90.5%
Common 141881
 
9.2%
Cyrillic 2878
 
0.2%
Hangul 209
 
< 0.1%
Han 184
 
< 0.1%
Greek 36
 
< 0.1%
Katakana 29
 
< 0.1%
Thai 25
 
< 0.1%
Arabic 11
 
< 0.1%
Hebrew 9
 
< 0.1%
Other values (9) 22
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 134241
 
9.7%
e 126440
 
9.1%
n 96332
 
6.9%
o 89826
 
6.5%
i 86485
 
6.2%
r 75326
 
5.4%
s 60058
 
4.3%
l 58981
 
4.2%
t 52812
 
3.8%
h 43888
 
3.2%
Other values (129) 564476
40.6%
Han
ValueCountFrequency (%)
6
 
3.3%
4
 
2.2%
4
 
2.2%
4
 
2.2%
4
 
2.2%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
Other values (123) 147
79.9%
Hangul
ValueCountFrequency (%)
10
 
4.8%
8
 
3.8%
6
 
2.9%
6
 
2.9%
5
 
2.4%
5
 
2.4%
4
 
1.9%
4
 
1.9%
4
 
1.9%
3
 
1.4%
Other values (110) 154
73.7%
Common
ValueCountFrequency (%)
125881
88.7%
. 4167
 
2.9%
& 2042
 
1.4%
- 1639
 
1.2%
' 945
 
0.7%
3 773
 
0.5%
0 670
 
0.5%
$ 643
 
0.5%
1 616
 
0.4%
2 595
 
0.4%
Other values (61) 3910
 
2.8%
Cyrillic
ValueCountFrequency (%)
и 484
16.8%
н 242
 
8.4%
а 223
 
7.7%
о 192
 
6.7%
у 154
 
5.4%
к 121
 
4.2%
л 120
 
4.2%
К 120
 
4.2%
т 116
 
4.0%
М 114
 
4.0%
Other values (43) 992
34.5%
Thai
ValueCountFrequency (%)
3
 
12.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (8) 8
32.0%
Katakana
ValueCountFrequency (%)
6
20.7%
4
13.8%
3
10.3%
3
10.3%
3
10.3%
3
10.3%
3
10.3%
1
 
3.4%
1
 
3.4%
1
 
3.4%
Arabic
ValueCountFrequency (%)
؞ 2
18.2%
م 2
18.2%
ي 2
18.2%
ؖ 1
9.1%
ا 1
9.1%
ص 1
9.1%
ل 1
9.1%
ه 1
9.1%
Hebrew
ValueCountFrequency (%)
י 2
22.2%
ו 2
22.2%
ג 1
11.1%
ן 1
11.1%
ב 1
11.1%
ר 1
11.1%
ד 1
11.1%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Tibetan
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Greek
ValueCountFrequency (%)
Π 18
50.0%
Δ 18
50.0%
Bengali
ValueCountFrequency (%)
2
66.7%
1
33.3%
Braille
ValueCountFrequency (%)
1
50.0%
1
50.0%
Georgian
ValueCountFrequency (%)
2
100.0%
Yi
ValueCountFrequency (%)
2
100.0%
Inherited
ValueCountFrequency (%)
̛ 1
100.0%
Tamil
ValueCountFrequency (%)
1
100.0%
Lao
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1526462
99.5%
None 4188
 
0.3%
Cyrillic 2878
 
0.2%
Hangul 209
 
< 0.1%
CJK 184
 
< 0.1%
Punctuation 79
 
< 0.1%
Katakana 38
 
< 0.1%
Thai 25
 
< 0.1%
Dingbats 23
 
< 0.1%
Arabic 11
 
< 0.1%
Other values (16) 52
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 134241
 
8.8%
e 126440
 
8.3%
125881
 
8.2%
n 96332
 
6.3%
o 89826
 
5.9%
i 86485
 
5.7%
r 75326
 
4.9%
s 60058
 
3.9%
l 58981
 
3.9%
t 52812
 
3.5%
Other values (83) 620080
40.6%
None
ValueCountFrequency (%)
é 1786
42.6%
ó 290
 
6.9%
á 280
 
6.7%
ñ 250
 
6.0%
í 225
 
5.4%
ü 110
 
2.6%
ö 101
 
2.4%
Ø 83
 
2.0%
ø 73
 
1.7%
ı 58
 
1.4%
Other values (89) 932
22.3%
Cyrillic
ValueCountFrequency (%)
и 484
16.8%
н 242
 
8.4%
а 223
 
7.7%
о 192
 
6.7%
у 154
 
5.4%
к 121
 
4.2%
л 120
 
4.2%
К 120
 
4.2%
т 116
 
4.0%
М 114
 
4.0%
Other values (43) 992
34.5%
Punctuation
ValueCountFrequency (%)
65
82.3%
5
 
6.3%
3
 
3.8%
3
 
3.8%
3
 
3.8%
Dingbats
ValueCountFrequency (%)
12
52.2%
8
34.8%
3
 
13.0%
Hangul
ValueCountFrequency (%)
10
 
4.8%
8
 
3.8%
6
 
2.9%
6
 
2.9%
5
 
2.4%
5
 
2.4%
4
 
1.9%
4
 
1.9%
4
 
1.9%
3
 
1.4%
Other values (110) 154
73.7%
Katakana
ValueCountFrequency (%)
9
23.7%
6
15.8%
4
10.5%
3
 
7.9%
3
 
7.9%
3
 
7.9%
3
 
7.9%
3
 
7.9%
1
 
2.6%
1
 
2.6%
Other values (2) 2
 
5.3%
Misc Symbols
ValueCountFrequency (%)
7
70.0%
2
 
20.0%
1
 
10.0%
CJK
ValueCountFrequency (%)
6
 
3.3%
4
 
2.2%
4
 
2.2%
4
 
2.2%
4
 
2.2%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
Other values (123) 147
79.9%
Thai
ValueCountFrequency (%)
3
 
12.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
2
 
8.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
1
 
4.0%
Other values (8) 8
32.0%
Bengali
ValueCountFrequency (%)
2
66.7%
1
33.3%
Arabic
ValueCountFrequency (%)
؞ 2
18.2%
م 2
18.2%
ي 2
18.2%
ؖ 1
9.1%
ا 1
9.1%
ص 1
9.1%
ل 1
9.1%
ه 1
9.1%
Hebrew
ValueCountFrequency (%)
י 2
22.2%
ו 2
22.2%
ג 1
11.1%
ן 1
11.1%
ב 1
11.1%
ר 1
11.1%
ד 1
11.1%
Georgian
ValueCountFrequency (%)
2
100.0%
Yi Syllables
ValueCountFrequency (%)
2
100.0%
Tibetan
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Hiragana
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Geometric Shapes
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%
Latin Ext Additional
ValueCountFrequency (%)
2
66.7%
1
33.3%
IPA Ext
ValueCountFrequency (%)
ɘ 1
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%
Diacriticals
ValueCountFrequency (%)
̛ 1
100.0%
Tamil
ValueCountFrequency (%)
1
100.0%
Braille
ValueCountFrequency (%)
1
50.0%
1
50.0%
Lao
ValueCountFrequency (%)
1
100.0%
Distinct130326
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size9.8 MiB
2023-06-28T16:04:43.339743image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length22
Median length22
Mean length22
Min length22

Characters and Unicode

Total characters2874586
Distinct characters62
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique129998 ?
Unique (%)99.5%

Sample

1st row2RM4jf1Xa9zPgMGRDiht8O
2nd row1tHDG53xJNGsItRA3vfVgs
3rd row6Wosx2euFPMT14UXiWudMy
4th row3J2Jpw61sO7l6Hc7qdYV91
5th row2jbYvQCyPgX3CdmAzeVeuS
ValueCountFrequency (%)
0ubafqn0hvwrcednov2szu 4
 
< 0.1%
1cvod6phuf31q08aazyjki 3
 
< 0.1%
47mtzdlkxsxwh1eljtyflo 3
 
< 0.1%
1afymoxqcyfxuosrckstsy 3
 
< 0.1%
569bgswqapyd8niuy67cal 3
 
< 0.1%
6eevqkt1zwhhzg08rfj7uc 3
 
< 0.1%
4fbwto3dj2qrymddov9rbb 3
 
< 0.1%
02mqj3mpw8mldmm2cqivb9 3
 
< 0.1%
7lkf8xrf0o74b3r2y41hzu 2
 
< 0.1%
4puwpntdejqwwa80ljvxxl 2
 
< 0.1%
Other values (130316) 130634
> 99.9%
2023-06-28T16:04:43.779282image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 61281
 
2.1%
1 61271
 
2.1%
6 61150
 
2.1%
5 61044
 
2.1%
0 61034
 
2.1%
4 60873
 
2.1%
2 60863
 
2.1%
7 57641
 
2.0%
m 44632
 
1.6%
o 44598
 
1.6%
Other values (52) 2300199
80.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1152044
40.1%
Uppercase Letter 1148710
40.0%
Decimal Number 573832
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
m 44632
 
3.9%
o 44598
 
3.9%
c 44590
 
3.9%
n 44555
 
3.9%
s 44549
 
3.9%
y 44507
 
3.9%
i 44503
 
3.9%
u 44497
 
3.9%
d 44440
 
3.9%
r 44410
 
3.9%
Other values (16) 706763
61.3%
Uppercase Letter
ValueCountFrequency (%)
R 44435
 
3.9%
D 44403
 
3.9%
Z 44378
 
3.9%
K 44373
 
3.9%
H 44333
 
3.9%
L 44331
 
3.9%
S 44326
 
3.9%
C 44312
 
3.9%
V 44305
 
3.9%
W 44285
 
3.9%
Other values (16) 705229
61.4%
Decimal Number
ValueCountFrequency (%)
3 61281
10.7%
1 61271
10.7%
6 61150
10.7%
5 61044
10.6%
0 61034
10.6%
4 60873
10.6%
2 60863
10.6%
7 57641
10.0%
8 44345
7.7%
9 44330
7.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 2300754
80.0%
Common 573832
 
20.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
m 44632
 
1.9%
o 44598
 
1.9%
c 44590
 
1.9%
n 44555
 
1.9%
s 44549
 
1.9%
y 44507
 
1.9%
i 44503
 
1.9%
u 44497
 
1.9%
d 44440
 
1.9%
R 44435
 
1.9%
Other values (42) 1855448
80.6%
Common
ValueCountFrequency (%)
3 61281
10.7%
1 61271
10.7%
6 61150
10.7%
5 61044
10.6%
0 61034
10.6%
4 60873
10.6%
2 60863
10.6%
7 57641
10.0%
8 44345
7.7%
9 44330
7.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2874586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 61281
 
2.1%
1 61271
 
2.1%
6 61150
 
2.1%
5 61044
 
2.1%
0 61034
 
2.1%
4 60873
 
2.1%
2 60863
 
2.1%
7 57641
 
2.0%
m 44632
 
1.6%
o 44598
 
1.6%
Other values (52) 2300199
80.0%
Distinct108889
Distinct (%)83.3%
Missing1
Missing (%)< 0.1%
Memory size10.1 MiB
2023-06-28T16:04:44.109084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length215
Median length149
Mean length20.136474
Min length1

Characters and Unicode

Total characters2631072
Distinct characters1625
Distinct categories21 ?
Distinct scripts14 ?
Distinct blocks27 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100353 ?
Unique (%)76.8%

Sample

1st rowBig Bank feat. 2 Chainz, Big Sean, Nicki Minaj
2nd rowBAND DRUM (feat. A$AP Rocky)
3rd rowRadio Silence
4th rowLactose
5th rowSame - Original mix
ValueCountFrequency (%)
18264
 
3.7%
the 10740
 
2.2%
in 8100
 
1.6%
feat 6564
 
1.3%
no 6146
 
1.2%
i 4962
 
1.0%
a 4854
 
1.0%
you 4841
 
1.0%
remix 4556
 
0.9%
of 4521
 
0.9%
Other values (53906) 419963
85.1%
2023-06-28T16:04:44.629251image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
362849
 
13.8%
e 218298
 
8.3%
a 155317
 
5.9%
o 152681
 
5.8%
i 133563
 
5.1%
n 126432
 
4.8%
r 115040
 
4.4%
t 115036
 
4.4%
l 79239
 
3.0%
s 77644
 
3.0%
Other values (1615) 1094973
41.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1622176
61.7%
Uppercase Letter 444438
 
16.9%
Space Separator 362849
 
13.8%
Other Punctuation 72762
 
2.8%
Decimal Number 57833
 
2.2%
Close Punctuation 19735
 
0.8%
Open Punctuation 19735
 
0.8%
Dash Punctuation 17222
 
0.7%
Other Letter 12639
 
0.5%
Final Punctuation 465
 
< 0.1%
Other values (11) 1218
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
429
 
3.4%
360
 
2.8%
329
 
2.6%
調 314
 
2.5%
228
 
1.8%
227
 
1.8%
198
 
1.6%
196
 
1.6%
194
 
1.5%
189
 
1.5%
Other values (1256) 9975
78.9%
Lowercase Letter
ValueCountFrequency (%)
e 218298
13.5%
a 155317
 
9.6%
o 152681
 
9.4%
i 133563
 
8.2%
n 126432
 
7.8%
r 115040
 
7.1%
t 115036
 
7.1%
l 79239
 
4.9%
s 77644
 
4.8%
u 57918
 
3.6%
Other values (137) 391008
24.1%
Uppercase Letter
ValueCountFrequency (%)
S 36729
 
8.3%
T 32017
 
7.2%
M 31999
 
7.2%
A 28939
 
6.5%
I 26770
 
6.0%
B 25682
 
5.8%
L 23272
 
5.2%
C 22230
 
5.0%
R 20212
 
4.5%
D 20048
 
4.5%
Other values (83) 176540
39.7%
Other Punctuation
ValueCountFrequency (%)
. 28205
38.8%
, 17056
23.4%
: 9396
 
12.9%
' 7053
 
9.7%
" 4355
 
6.0%
& 3105
 
4.3%
/ 1448
 
2.0%
! 862
 
1.2%
? 592
 
0.8%
257
 
0.4%
Other values (16) 433
 
0.6%
Other Symbol
ValueCountFrequency (%)
° 24
18.5%
18
13.8%
18
13.8%
18
13.8%
17
13.1%
9
 
6.9%
® 8
 
6.2%
6
 
4.6%
4
 
3.1%
2
 
1.5%
Other values (6) 6
 
4.6%
Nonspacing Mark
ValueCountFrequency (%)
24
19.2%
22
17.6%
21
16.8%
15
12.0%
9
 
7.2%
7
 
5.6%
̸ 7
 
5.6%
̡ 7
 
5.6%
5
 
4.0%
4
 
3.2%
Other values (2) 4
 
3.2%
Decimal Number
ValueCountFrequency (%)
1 9900
17.1%
2 9352
16.2%
8 6709
11.6%
0 6340
11.0%
4 4792
8.3%
3 4678
8.1%
5 4543
7.9%
6 3934
 
6.8%
9 3933
 
6.8%
7 3645
 
6.3%
Math Symbol
ValueCountFrequency (%)
+ 65
28.0%
> 46
19.8%
< 46
19.8%
| 42
18.1%
~ 19
 
8.2%
= 11
 
4.7%
× 2
 
0.9%
1
 
0.4%
Open Punctuation
ValueCountFrequency (%)
( 18534
93.9%
[ 1143
 
5.8%
40
 
0.2%
15
 
0.1%
1
 
< 0.1%
1
 
< 0.1%
{ 1
 
< 0.1%
Close Punctuation
ValueCountFrequency (%)
) 18535
93.9%
] 1143
 
5.8%
40
 
0.2%
15
 
0.1%
1
 
< 0.1%
} 1
 
< 0.1%
Dash Punctuation
ValueCountFrequency (%)
- 17145
99.6%
39
 
0.2%
16
 
0.1%
12
 
0.1%
7
 
< 0.1%
3
 
< 0.1%
Modifier Letter
ValueCountFrequency (%)
276
97.5%
3
 
1.1%
2
 
0.7%
ʼ 1
 
0.4%
ˌ 1
 
0.4%
Modifier Symbol
ValueCountFrequency (%)
´ 32
65.3%
` 9
 
18.4%
¨ 6
 
12.2%
^ 2
 
4.1%
Final Punctuation
ValueCountFrequency (%)
418
89.9%
45
 
9.7%
» 2
 
0.4%
Currency Symbol
ValueCountFrequency (%)
$ 236
96.7%
¢ 7
 
2.9%
¥ 1
 
0.4%
Initial Punctuation
ValueCountFrequency (%)
53
84.1%
9
 
14.3%
« 1
 
1.6%
Format
ValueCountFrequency (%)
3
42.9%
2
28.6%
­ 2
28.6%
Letter Number
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Space Separator
ValueCountFrequency (%)
362849
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 61
100.0%
Enclosing Mark
ValueCountFrequency (%)
҉ 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2061920
78.4%
Common 551658
 
21.0%
Katakana 5026
 
0.2%
Cyrillic 4683
 
0.2%
Han 4422
 
0.2%
Hiragana 1442
 
0.1%
Hangul 803
 
< 0.1%
Hebrew 485
 
< 0.1%
Thai 485
 
< 0.1%
Arabic 90
 
< 0.1%
Other values (4) 58
 
< 0.1%

Most frequent character per script

Han
ValueCountFrequency (%)
360
 
8.1%
調 314
 
7.1%
228
 
5.2%
196
 
4.4%
187
 
4.2%
175
 
4.0%
175
 
4.0%
129
 
2.9%
120
 
2.7%
113
 
2.6%
Other values (754) 2425
54.8%
Hangul
ValueCountFrequency (%)
22
 
2.7%
18
 
2.2%
17
 
2.1%
15
 
1.9%
13
 
1.6%
13
 
1.6%
13
 
1.6%
12
 
1.5%
12
 
1.5%
11
 
1.4%
Other values (262) 657
81.8%
Latin
ValueCountFrequency (%)
e 218298
 
10.6%
a 155317
 
7.5%
o 152681
 
7.4%
i 133563
 
6.5%
n 126432
 
6.1%
r 115040
 
5.6%
t 115036
 
5.6%
l 79239
 
3.8%
s 77644
 
3.8%
u 57918
 
2.8%
Other values (152) 830752
40.3%
Common
ValueCountFrequency (%)
362849
65.8%
. 28205
 
5.1%
) 18535
 
3.4%
( 18534
 
3.4%
- 17145
 
3.1%
, 17056
 
3.1%
1 9900
 
1.8%
: 9396
 
1.7%
2 9352
 
1.7%
' 7053
 
1.3%
Other values (90) 53633
 
9.7%
Katakana
ValueCountFrequency (%)
429
 
8.5%
227
 
4.5%
198
 
3.9%
194
 
3.9%
189
 
3.8%
186
 
3.7%
180
 
3.6%
166
 
3.3%
166
 
3.3%
164
 
3.3%
Other values (64) 2927
58.2%
Hiragana
ValueCountFrequency (%)
329
22.8%
106
 
7.4%
83
 
5.8%
78
 
5.4%
75
 
5.2%
60
 
4.2%
38
 
2.6%
32
 
2.2%
32
 
2.2%
32
 
2.2%
Other values (59) 577
40.0%
Cyrillic
ValueCountFrequency (%)
о 447
 
9.5%
а 385
 
8.2%
е 358
 
7.6%
и 260
 
5.6%
н 249
 
5.3%
т 237
 
5.1%
л 194
 
4.1%
р 188
 
4.0%
с 179
 
3.8%
в 149
 
3.2%
Other values (56) 2037
43.5%
Thai
ValueCountFrequency (%)
34
 
7.0%
32
 
6.6%
29
 
6.0%
24
 
4.9%
23
 
4.7%
22
 
4.5%
21
 
4.3%
19
 
3.9%
16
 
3.3%
16
 
3.3%
Other values (39) 249
51.3%
Hebrew
ValueCountFrequency (%)
ו 56
 
11.5%
י 55
 
11.3%
ה 40
 
8.2%
ל 34
 
7.0%
א 31
 
6.4%
ת 28
 
5.8%
מ 28
 
5.8%
ב 27
 
5.6%
ם 21
 
4.3%
ש 21
 
4.3%
Other values (17) 144
29.7%
Arabic
ValueCountFrequency (%)
ل 15
16.7%
ا 15
16.7%
ر 7
 
7.8%
ك 6
 
6.7%
م 5
 
5.6%
ب 5
 
5.6%
ن 5
 
5.6%
و 4
 
4.4%
د 3
 
3.3%
ح 3
 
3.3%
Other values (12) 22
24.4%
Greek
ValueCountFrequency (%)
φ 18
50.0%
α 3
 
8.3%
Σ 2
 
5.6%
γ 1
 
2.8%
έ 1
 
2.8%
ν 1
 
2.8%
ε 1
 
2.8%
σ 1
 
2.8%
ι 1
 
2.8%
ς 1
 
2.8%
Other values (6) 6
 
16.7%
Inherited
ValueCountFrequency (%)
̸ 7
50.0%
̡ 7
50.0%
Sinhala
ValueCountFrequency (%)
7
100.0%
Georgian
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2605564
99.0%
None 6759
 
0.3%
Katakana 5559
 
0.2%
Cyrillic 4683
 
0.2%
CJK 4419
 
0.2%
Hiragana 1442
 
0.1%
Hangul 803
 
< 0.1%
Punctuation 638
 
< 0.1%
Hebrew 485
 
< 0.1%
Thai 485
 
< 0.1%
Other values (17) 235
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
362849
 
13.9%
e 218298
 
8.4%
a 155317
 
6.0%
o 152681
 
5.9%
i 133563
 
5.1%
n 126432
 
4.9%
r 115040
 
4.4%
t 115036
 
4.4%
l 79239
 
3.0%
s 77644
 
3.0%
Other values (85) 1069465
41.0%
None
ValueCountFrequency (%)
é 1376
20.4%
ü 656
 
9.7%
ó 585
 
8.7%
á 410
 
6.1%
í 376
 
5.6%
ö 355
 
5.3%
ä 314
 
4.6%
ñ 305
 
4.5%
è 228
 
3.4%
É 220
 
3.3%
Other values (124) 1934
28.6%
Cyrillic
ValueCountFrequency (%)
о 447
 
9.5%
а 385
 
8.2%
е 358
 
7.6%
и 260
 
5.6%
н 249
 
5.3%
т 237
 
5.1%
л 194
 
4.1%
р 188
 
4.0%
с 179
 
3.8%
в 149
 
3.2%
Other values (56) 2037
43.5%
Katakana
ValueCountFrequency (%)
429
 
7.7%
276
 
5.0%
257
 
4.6%
227
 
4.1%
198
 
3.6%
194
 
3.5%
189
 
3.4%
186
 
3.3%
180
 
3.2%
166
 
3.0%
Other values (66) 3257
58.6%
Punctuation
ValueCountFrequency (%)
418
65.5%
53
 
8.3%
45
 
7.1%
39
 
6.1%
24
 
3.8%
16
 
2.5%
12
 
1.9%
11
 
1.7%
9
 
1.4%
3
 
0.5%
Other values (5) 8
 
1.3%
CJK
ValueCountFrequency (%)
360
 
8.1%
調 314
 
7.1%
228
 
5.2%
196
 
4.4%
187
 
4.2%
175
 
4.0%
175
 
4.0%
129
 
2.9%
120
 
2.7%
113
 
2.6%
Other values (753) 2422
54.8%
Hiragana
ValueCountFrequency (%)
329
22.8%
106
 
7.4%
83
 
5.8%
78
 
5.4%
75
 
5.2%
60
 
4.2%
38
 
2.6%
32
 
2.2%
32
 
2.2%
32
 
2.2%
Other values (59) 577
40.0%
Hebrew
ValueCountFrequency (%)
ו 56
 
11.5%
י 55
 
11.3%
ה 40
 
8.2%
ל 34
 
7.0%
א 31
 
6.4%
ת 28
 
5.8%
מ 28
 
5.8%
ב 27
 
5.6%
ם 21
 
4.3%
ש 21
 
4.3%
Other values (17) 144
29.7%
Thai
ValueCountFrequency (%)
34
 
7.0%
32
 
6.6%
29
 
6.0%
24
 
4.9%
23
 
4.7%
22
 
4.5%
21
 
4.3%
19
 
3.9%
16
 
3.3%
16
 
3.3%
Other values (39) 249
51.3%
Hangul
ValueCountFrequency (%)
22
 
2.7%
18
 
2.2%
17
 
2.1%
15
 
1.9%
13
 
1.6%
13
 
1.6%
13
 
1.6%
12
 
1.5%
12
 
1.5%
11
 
1.4%
Other values (262) 657
81.8%
Block Elements
ValueCountFrequency (%)
18
100.0%
Misc Technical
ValueCountFrequency (%)
18
100.0%
Box Drawing
ValueCountFrequency (%)
18
50.0%
17
47.2%
1
 
2.8%
Arabic
ValueCountFrequency (%)
ل 15
16.7%
ا 15
16.7%
ر 7
 
7.8%
ك 6
 
6.7%
م 5
 
5.6%
ب 5
 
5.6%
ن 5
 
5.6%
و 4
 
4.4%
د 3
 
3.3%
ح 3
 
3.3%
Other values (12) 22
24.4%
Geometric Shapes
ValueCountFrequency (%)
9
56.2%
6
37.5%
1
 
6.2%
Diacriticals
ValueCountFrequency (%)
̸ 7
50.0%
̡ 7
50.0%
Sinhala
ValueCountFrequency (%)
7
100.0%
Misc Symbols
ValueCountFrequency (%)
4
57.1%
1
 
14.3%
1
 
14.3%
1
 
14.3%
Latin Ext Additional
ValueCountFrequency (%)
3
17.6%
3
17.6%
2
11.8%
2
11.8%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
1
 
5.9%
ế 1
 
5.9%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
Number Forms
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Math Operators
ValueCountFrequency (%)
1
100.0%
Modifier Letters
ValueCountFrequency (%)
ʼ 1
50.0%
ˌ 1
50.0%
Greek Ext
ValueCountFrequency (%)
1
100.0%
IPA Ext
ValueCountFrequency (%)
ə 1
100.0%
Arrows
ValueCountFrequency (%)
1
100.0%
Georgian
ValueCountFrequency (%)
1
100.0%

acousticness
Real number (ℝ)

HIGH CORRELATION 

Distinct4908
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.34250035
Minimum0
Maximum0.996
Zeros46
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:44.802886image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.000394
Q10.0316
median0.203
Q30.636
95-th percentile0.981
Maximum0.996
Range0.996
Interquartile range (IQR)0.6044

Descriptive statistics

Standard deviation0.3456406
Coefficient of variation (CV)1.0091686
Kurtosis-1.0333797
Mean0.34250035
Median Absolute Deviation (MAD)0.19698
Skewness0.68404166
Sum44752.123
Variance0.11946743
MonotonicityNot monotonic
2023-06-28T16:04:44.964769image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.995 738
 
0.6%
0.994 705
 
0.5%
0.993 613
 
0.5%
0.992 550
 
0.4%
0.991 508
 
0.4%
0.99 466
 
0.4%
0.989 424
 
0.3%
0.988 383
 
0.3%
0.987 364
 
0.3%
0.985 313
 
0.2%
Other values (4898) 125599
96.1%
ValueCountFrequency (%)
0 46
< 0.1%
1 × 10-61
 
< 0.1%
1.01 × 10-61
 
< 0.1%
1.03 × 10-61
 
< 0.1%
1.04 × 10-63
 
< 0.1%
1.06 × 10-61
 
< 0.1%
1.07 × 10-61
 
< 0.1%
1.1 × 10-61
 
< 0.1%
1.11 × 10-62
 
< 0.1%
1.15 × 10-61
 
< 0.1%
ValueCountFrequency (%)
0.996 261
 
0.2%
0.995 738
0.6%
0.994 705
0.5%
0.993 613
0.5%
0.992 550
0.4%
0.991 508
0.4%
0.99 466
0.4%
0.989 424
0.3%
0.988 383
0.3%
0.987 364
0.3%

danceability
Real number (ℝ)

Distinct1257
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58146836
Minimum0
Maximum0.996
Zeros292
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:45.133940image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22
Q10.459
median0.605
Q30.727
95-th percentile0.851
Maximum0.996
Range0.996
Interquartile range (IQR)0.268

Descriptive statistics

Standard deviation0.19007688
Coefficient of variation (CV)0.32689118
Kurtosis-0.2736465
Mean0.58146836
Median Absolute Deviation (MAD)0.132
Skewness-0.50846114
Sum75976.4
Variance0.036129218
MonotonicityNot monotonic
2023-06-28T16:04:45.292420image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.664 306
 
0.2%
0.684 305
 
0.2%
0.75 303
 
0.2%
0.657 302
 
0.2%
0.659 299
 
0.2%
0.714 298
 
0.2%
0.678 297
 
0.2%
0.708 296
 
0.2%
0.699 295
 
0.2%
0.691 293
 
0.2%
Other values (1247) 127669
97.7%
ValueCountFrequency (%)
0 292
0.2%
0.0237 1
 
< 0.1%
0.0513 1
 
< 0.1%
0.0516 1
 
< 0.1%
0.0532 2
 
< 0.1%
0.0533 1
 
< 0.1%
0.0542 1
 
< 0.1%
0.055 1
 
< 0.1%
0.0555 2
 
< 0.1%
0.0559 1
 
< 0.1%
ValueCountFrequency (%)
0.996 1
 
< 0.1%
0.986 5
< 0.1%
0.985 4
< 0.1%
0.984 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 5
< 0.1%
0.98 4
< 0.1%
0.978 3
 
< 0.1%
0.977 8
< 0.1%

duration_ms
Real number (ℝ)

Distinct77897
Distinct (%)59.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean212633.12
Minimum3203
Maximum5610020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:45.475811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3203
5-th percentile80292.1
Q1163922.5
median201901
Q3241047.5
95-th percentile361077.8
Maximum5610020
Range5606817
Interquartile range (IQR)77125

Descriptive statistics

Standard deviation123155.06
Coefficient of variation (CV)0.5791904
Kurtosis302.10806
Mean212633.12
Median Absolute Deviation (MAD)38501
Skewness11.643607
Sum2.7783282 × 1010
Variance1.516717 × 1010
MonotonicityNot monotonic
2023-06-28T16:04:45.638936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144405 346
 
0.3%
192000 185
 
0.1%
180000 136
 
0.1%
240000 135
 
0.1%
60000 98
 
0.1%
216000 97
 
0.1%
160000 92
 
0.1%
168000 86
 
0.1%
200000 79
 
0.1%
210000 79
 
0.1%
Other values (77887) 129330
99.0%
ValueCountFrequency (%)
3203 1
< 0.1%
3677 1
< 0.1%
4160 1
< 0.1%
4556 1
< 0.1%
4867 1
< 0.1%
5147 1
< 0.1%
5747 1
< 0.1%
5827 1
< 0.1%
7520 1
< 0.1%
7573 1
< 0.1%
ValueCountFrequency (%)
5610020 1
< 0.1%
5040048 1
< 0.1%
4893656 1
< 0.1%
4891333 1
< 0.1%
4830606 1
< 0.1%
4830011 1
< 0.1%
4661991 1
< 0.1%
4303366 1
< 0.1%
3777432 1
< 0.1%
3769399 1
< 0.1%

energy
Real number (ℝ)

HIGH CORRELATION 

Distinct2571
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.56919595
Minimum0
Maximum1
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:45.813030image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.072
Q10.396
median0.603
Q30.775
95-th percentile0.941
Maximum1
Range1
Interquartile range (IQR)0.379

Descriptive statistics

Standard deviation0.26031169
Coefficient of variation (CV)0.45733229
Kurtosis-0.7023312
Mean0.56919595
Median Absolute Deviation (MAD)0.187
Skewness-0.43608757
Sum74372.851
Variance0.067762175
MonotonicityNot monotonic
2023-06-28T16:04:46.221632image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.666 248
 
0.2%
0.726 233
 
0.2%
0.723 232
 
0.2%
0.694 228
 
0.2%
0.717 227
 
0.2%
0.577 224
 
0.2%
0.719 224
 
0.2%
0.703 223
 
0.2%
0.73 223
 
0.2%
0.669 221
 
0.2%
Other values (2561) 128380
98.3%
ValueCountFrequency (%)
0 19
< 0.1%
1.9 × 10-51
 
< 0.1%
1.94 × 10-51
 
< 0.1%
1.95 × 10-53
 
< 0.1%
1.96 × 10-52
 
< 0.1%
1.97 × 10-52
 
< 0.1%
1.98 × 10-52
 
< 0.1%
1.99 × 10-55
 
< 0.1%
2 × 10-57
 
< 0.1%
2.01 × 10-517
< 0.1%
ValueCountFrequency (%)
1 190
0.1%
0.999 137
0.1%
0.998 79
0.1%
0.997 77
0.1%
0.996 76
 
0.1%
0.995 102
0.1%
0.994 85
0.1%
0.993 92
0.1%
0.992 67
 
0.1%
0.991 75
 
0.1%

instrumentalness
Real number (ℝ)

ZEROS 

Distinct5387
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22401834
Minimum0
Maximum1
Zeros42133
Zeros (%)32.2%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:46.393913image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.000149
Q30.44
95-th percentile0.929
Maximum1
Range1
Interquartile range (IQR)0.44

Descriptive statistics

Standard deviation0.36032793
Coefficient of variation (CV)1.6084751
Kurtosis-0.45854504
Mean0.22401834
Median Absolute Deviation (MAD)0.000149
Skewness1.1698753
Sum29270.909
Variance0.12983621
MonotonicityNot monotonic
2023-06-28T16:04:46.551086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42133
32.2%
0.918 220
 
0.2%
0.92 216
 
0.2%
0.915 215
 
0.2%
0.914 206
 
0.2%
0.906 198
 
0.2%
0.933 197
 
0.2%
0.904 196
 
0.2%
0.898 196
 
0.2%
0.927 195
 
0.1%
Other values (5377) 86691
66.3%
ValueCountFrequency (%)
0 42133
32.2%
1 × 10-619
 
< 0.1%
1.01 × 10-636
 
< 0.1%
1.02 × 10-650
 
< 0.1%
1.03 × 10-633
 
< 0.1%
1.04 × 10-642
 
< 0.1%
1.05 × 10-648
 
< 0.1%
1.06 × 10-654
 
< 0.1%
1.07 × 10-649
 
< 0.1%
1.08 × 10-642
 
< 0.1%
ValueCountFrequency (%)
1 24
< 0.1%
0.999 18
< 0.1%
0.998 15
< 0.1%
0.997 23
< 0.1%
0.996 28
< 0.1%
0.995 20
< 0.1%
0.994 26
< 0.1%
0.993 22
< 0.1%
0.992 37
< 0.1%
0.991 20
< 0.1%

key
Real number (ℝ)

ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2318943
Minimum0
Maximum11
Zeros14972
Zeros (%)11.5%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:46.710536image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.6027011
Coefficient of variation (CV)0.68860358
Kurtosis-1.3199843
Mean5.2318943
Median Absolute Deviation (MAD)3
Skewness0.020392001
Sum683615
Variance12.979455
MonotonicityNot monotonic
2023-06-28T16:04:46.836676image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 15348
11.7%
0 14972
11.5%
7 13930
10.7%
2 12363
9.5%
9 12029
9.2%
5 10816
8.3%
11 10341
7.9%
8 9299
7.1%
10 9298
7.1%
4 9045
6.9%
Other values (2) 13222
10.1%
ValueCountFrequency (%)
0 14972
11.5%
1 15348
11.7%
2 12363
9.5%
3 4315
 
3.3%
4 9045
6.9%
5 10816
8.3%
6 8907
6.8%
7 13930
10.7%
8 9299
7.1%
9 12029
9.2%
ValueCountFrequency (%)
11 10341
7.9%
10 9298
7.1%
9 12029
9.2%
8 9299
7.1%
7 13930
10.7%
6 8907
6.8%
5 10816
8.3%
4 9045
6.9%
3 4315
 
3.3%
2 12363
9.5%

liveness
Real number (ℝ)

Distinct1717
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19488627
Minimum0
Maximum0.999
Zeros31
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:47.009996image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.064
Q10.0975
median0.124
Q30.236
95-th percentile0.574
Maximum0.999
Range0.999
Interquartile range (IQR)0.1385

Descriptive statistics

Standard deviation0.16773329
Coefficient of variation (CV)0.86067269
Kurtosis5.707009
Mean0.19488627
Median Absolute Deviation (MAD)0.0404
Skewness2.2975707
Sum25464.424
Variance0.028134455
MonotonicityNot monotonic
2023-06-28T16:04:47.187811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 1873
 
1.4%
0.11 1670
 
1.3%
0.112 1603
 
1.2%
0.108 1600
 
1.2%
0.109 1600
 
1.2%
0.107 1487
 
1.1%
0.105 1430
 
1.1%
0.106 1401
 
1.1%
0.104 1377
 
1.1%
0.103 1347
 
1.0%
Other values (1707) 115275
88.2%
ValueCountFrequency (%)
0 31
< 0.1%
0.0113 1
 
< 0.1%
0.0118 1
 
< 0.1%
0.0131 1
 
< 0.1%
0.0138 1
 
< 0.1%
0.0139 1
 
< 0.1%
0.0148 2
 
< 0.1%
0.0149 1
 
< 0.1%
0.0153 1
 
< 0.1%
0.0158 1
 
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.996 1
 
< 0.1%
0.995 2
< 0.1%
0.994 3
< 0.1%
0.993 2
< 0.1%
0.992 2
< 0.1%
0.991 4
< 0.1%
0.99 2
< 0.1%
0.989 4
< 0.1%
0.988 3
< 0.1%

loudness
Real number (ℝ)

HIGH CORRELATION 

Distinct25888
Distinct (%)19.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-9.9740056
Minimum-60
Maximum1.806
Zeros1
Zeros (%)< 0.1%
Negative130601
Negative (%)> 99.9%
Memory size1020.9 KiB
2023-06-28T16:04:47.364905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum-60
5-th percentile-24.2195
Q1-11.898
median-7.979
Q3-5.684
95-th percentile-3.442
Maximum1.806
Range61.806
Interquartile range (IQR)6.214

Descriptive statistics

Standard deviation6.544379
Coefficient of variation (CV)-0.65614351
Kurtosis3.9131332
Mean-9.9740056
Median Absolute Deviation (MAD)2.789
Skewness-1.8039098
Sum-1303233.5
Variance42.828897
MonotonicityNot monotonic
2023-06-28T16:04:47.543811image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-5.682 30
 
< 0.1%
-6.246 30
 
< 0.1%
-5.488 29
 
< 0.1%
-5.448 28
 
< 0.1%
-6.541 28
 
< 0.1%
-6.141 27
 
< 0.1%
-6.099 27
 
< 0.1%
-5.735 27
 
< 0.1%
-6.294 27
 
< 0.1%
-6.113 27
 
< 0.1%
Other values (25878) 130383
99.8%
ValueCountFrequency (%)
-60 12
< 0.1%
-58.656 1
 
< 0.1%
-58.48 1
 
< 0.1%
-55.909 1
 
< 0.1%
-54.574 1
 
< 0.1%
-53.489 1
 
< 0.1%
-53.445 1
 
< 0.1%
-53.28 1
 
< 0.1%
-53.1 1
 
< 0.1%
-52.058 3
 
< 0.1%
ValueCountFrequency (%)
1.806 1
< 0.1%
1.75 1
< 0.1%
1.187 1
< 0.1%
0.935 1
< 0.1%
0.905 1
< 0.1%
0.843 1
< 0.1%
0.805 1
< 0.1%
0.738 1
< 0.1%
0.726 1
< 0.1%
0.681 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
1
79409 
0
51254 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130663
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 79409
60.8%
0 51254
39.2%

Length

2023-06-28T16:04:47.716656image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T16:04:47.874347image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
1 79409
60.8%
0 51254
39.2%

Most occurring characters

ValueCountFrequency (%)
1 79409
60.8%
0 51254
39.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 79409
60.8%
0 51254
39.2%

Most occurring scripts

ValueCountFrequency (%)
Common 130663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 79409
60.8%
0 51254
39.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 79409
60.8%
0 51254
39.2%

speechiness
Real number (ℝ)

Distinct1616
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.11201497
Minimum0
Maximum0.966
Zeros292
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:48.027542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0294
Q10.0389
median0.0559
Q30.129
95-th percentile0.377
Maximum0.966
Range0.966
Interquartile range (IQR)0.0901

Descriptive statistics

Standard deviation0.12432693
Coefficient of variation (CV)1.1099136
Kurtosis7.9341359
Mean0.11201497
Median Absolute Deviation (MAD)0.0223
Skewness2.4855605
Sum14636.212
Variance0.015457186
MonotonicityNot monotonic
2023-06-28T16:04:48.205817image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0376 314
 
0.2%
0.0359 309
 
0.2%
0.0378 308
 
0.2%
0.0358 308
 
0.2%
0.0366 307
 
0.2%
0.0374 306
 
0.2%
0.0387 305
 
0.2%
0.0367 304
 
0.2%
0.031 302
 
0.2%
0.0342 301
 
0.2%
Other values (1606) 127599
97.7%
ValueCountFrequency (%)
0 292
0.2%
0.0221 1
 
< 0.1%
0.0224 1
 
< 0.1%
0.0225 1
 
< 0.1%
0.0226 1
 
< 0.1%
0.0227 1
 
< 0.1%
0.0228 5
 
< 0.1%
0.0229 6
 
< 0.1%
0.023 4
 
< 0.1%
0.0231 4
 
< 0.1%
ValueCountFrequency (%)
0.966 1
 
< 0.1%
0.964 1
 
< 0.1%
0.961 1
 
< 0.1%
0.958 4
< 0.1%
0.957 3
< 0.1%
0.956 2
 
< 0.1%
0.955 2
 
< 0.1%
0.954 4
< 0.1%
0.953 3
< 0.1%
0.952 6
< 0.1%

tempo
Real number (ℝ)

Distinct57314
Distinct (%)43.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.47335
Minimum0
Maximum249.983
Zeros292
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:48.383074image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile75.014
Q196.014
median120.027
Q3139.642
95-th percentile173.2849
Maximum249.983
Range249.983
Interquartile range (IQR)43.628

Descriptive statistics

Standard deviation30.159636
Coefficient of variation (CV)0.25243818
Kurtosis0.064282418
Mean119.47335
Median Absolute Deviation (MAD)20.324
Skewness0.15218079
Sum15610747
Variance909.60365
MonotonicityNot monotonic
2023-06-28T16:04:48.575327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 292
 
0.2%
120.006 59
 
< 0.1%
119.999 57
 
< 0.1%
120.002 55
 
< 0.1%
119.997 54
 
< 0.1%
120.003 53
 
< 0.1%
119.996 50
 
< 0.1%
120.005 50
 
< 0.1%
120.012 50
 
< 0.1%
120.011 49
 
< 0.1%
Other values (57304) 129894
99.4%
ValueCountFrequency (%)
0 292
0.2%
31.608 1
 
< 0.1%
31.847 1
 
< 0.1%
32.001 1
 
< 0.1%
32.095 1
 
< 0.1%
32.317 1
 
< 0.1%
32.732 1
 
< 0.1%
32.922 1
 
< 0.1%
33.003 1
 
< 0.1%
33.108 1
 
< 0.1%
ValueCountFrequency (%)
249.983 1
< 0.1%
248.321 1
< 0.1%
247.951 1
< 0.1%
247.916 1
< 0.1%
246.203 1
< 0.1%
246.055 1
< 0.1%
245.379 1
< 0.1%
245.327 1
< 0.1%
244.51 1
< 0.1%
241.234 1
< 0.1%

time_signature
Categorical

IMBALANCE 

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size7.2 MiB
4
112652 
3
12666 
5
 
3297
1
 
1749
0
 
299

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters130663
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 112652
86.2%
3 12666
 
9.7%
5 3297
 
2.5%
1 1749
 
1.3%
0 299
 
0.2%

Length

2023-06-28T16:04:48.765977image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-06-28T16:04:48.934295image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
4 112652
86.2%
3 12666
 
9.7%
5 3297
 
2.5%
1 1749
 
1.3%
0 299
 
0.2%

Most occurring characters

ValueCountFrequency (%)
4 112652
86.2%
3 12666
 
9.7%
5 3297
 
2.5%
1 1749
 
1.3%
0 299
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 130663
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 112652
86.2%
3 12666
 
9.7%
5 3297
 
2.5%
1 1749
 
1.3%
0 299
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Common 130663
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 112652
86.2%
3 12666
 
9.7%
5 3297
 
2.5%
1 1749
 
1.3%
0 299
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 130663
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 112652
86.2%
3 12666
 
9.7%
5 3297
 
2.5%
1 1749
 
1.3%
0 299
 
0.2%

valence
Real number (ℝ)

Distinct1918
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43963029
Minimum0
Maximum1
Zeros334
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:49.102971image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04891
Q10.224
median0.42
Q30.638
95-th percentile0.896
Maximum1
Range1
Interquartile range (IQR)0.414

Descriptive statistics

Standard deviation0.25907946
Coefficient of variation (CV)0.58931212
Kurtosis-0.93601216
Mean0.43963029
Median Absolute Deviation (MAD)0.206
Skewness0.23706423
Sum57443.412
Variance0.067122165
MonotonicityNot monotonic
2023-06-28T16:04:49.276800image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 × 10-5391
 
0.3%
0 334
 
0.3%
0.962 262
 
0.2%
0.961 227
 
0.2%
0.326 209
 
0.2%
0.35 208
 
0.2%
0.346 201
 
0.2%
0.34 201
 
0.2%
0.348 199
 
0.2%
0.963 199
 
0.2%
Other values (1908) 128232
98.1%
ValueCountFrequency (%)
0 334
0.3%
1 × 10-5391
0.3%
4.25 × 10-51
 
< 0.1%
7.01 × 10-51
 
< 0.1%
0.000134 1
 
< 0.1%
0.000149 1
 
< 0.1%
0.000154 1
 
< 0.1%
0.000407 1
 
< 0.1%
0.000429 1
 
< 0.1%
0.000439 1
 
< 0.1%
ValueCountFrequency (%)
1 9
< 0.1%
0.999 3
 
< 0.1%
0.998 1
 
< 0.1%
0.997 1
 
< 0.1%
0.996 4
< 0.1%
0.995 3
 
< 0.1%
0.994 3
 
< 0.1%
0.993 5
< 0.1%
0.991 3
 
< 0.1%
0.99 2
 
< 0.1%

popularity
Real number (ℝ)

ZEROS 

Distinct100
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.208988
Minimum0
Maximum100
Zeros18889
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size1020.9 KiB
2023-06-28T16:04:49.484756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median22
Q338
95-th percentile60
Maximum100
Range100
Interquartile range (IQR)31

Descriptive statistics

Standard deviation19.713191
Coefficient of variation (CV)0.81429223
Kurtosis-0.4782102
Mean24.208988
Median Absolute Deviation (MAD)15
Skewness0.57823086
Sum3163219
Variance388.6099
MonotonicityNot monotonic
2023-06-28T16:04:49.640434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 18889
 
14.5%
1 3217
 
2.5%
19 2537
 
1.9%
22 2525
 
1.9%
2 2517
 
1.9%
23 2436
 
1.9%
20 2434
 
1.9%
18 2412
 
1.8%
17 2377
 
1.8%
21 2360
 
1.8%
Other values (90) 88959
68.1%
ValueCountFrequency (%)
0 18889
14.5%
1 3217
 
2.5%
2 2517
 
1.9%
3 2033
 
1.6%
4 1831
 
1.4%
5 1868
 
1.4%
6 1900
 
1.5%
7 1889
 
1.4%
8 1916
 
1.5%
9 2091
 
1.6%
ValueCountFrequency (%)
100 1
 
< 0.1%
98 4
 
< 0.1%
97 1
 
< 0.1%
96 7
< 0.1%
95 6
 
< 0.1%
94 11
< 0.1%
93 6
 
< 0.1%
92 7
< 0.1%
91 17
< 0.1%
90 9
< 0.1%

Interactions

2023-06-28T16:04:39.311604image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:18.572187image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.528003image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.226177image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.101094image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.772648image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:27.619317image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:29.725038image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:31.626713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:33.571578image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:35.489292image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.320916image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:39.459341image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:18.763859image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.662934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.357419image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.268385image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.934703image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:27.772037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:29.889329image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:31.796798image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:33.728037image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:35.640537image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.458462image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:39.607973image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:18.935009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.793694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.506937image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.408303image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.079646image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:27.920997image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.038629image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:31.956542image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:33.887063image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:35.803651image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.624633image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:39.759369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:19.117548image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.949349image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.655964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.541473image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.230692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:28.077958image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.208640image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:32.117044image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.056787image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:35.957084image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.784955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:39.908675image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:19.281939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.098540image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.812272image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.682319image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.381549image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:28.228262image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.359260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:32.287033image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.189009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:36.103176image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.938934image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.067975image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:19.545015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.246818image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.953480image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.812543image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.526023image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:28.373649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.513407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:32.449627image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.355697image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:36.259735image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:38.079974image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.213502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:19.690917image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.381390image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:23.101939image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:24.962288image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.674936image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:28.746779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.639407image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:32.611879image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.524788image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:36.429829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:38.227739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.358217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:19.821128image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.514988image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:23.263270image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.106333image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.822582image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:28.913644image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.818034image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:32.760054image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.681815image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:36.605689image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:38.370874image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.513779image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:19.966384image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.657768image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:23.417678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.252259image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:26.984036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:29.081506image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:30.979998image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:32.930004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.844244image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:36.754039image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:38.519431image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.657192image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.105767image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.801091image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:23.550654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.377947image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:27.144075image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:29.250649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:31.143701image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:33.096753image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:34.997760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:36.893425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:38.860952image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.789120image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.256492image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:21.947009image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:23.736079image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.522713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:27.311008image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:29.410127image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:31.316795image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:33.261454image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:35.164015image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.031790image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:39.021791image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:40.938693image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:20.391573image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:22.093534image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:23.930403image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:25.644433image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:27.469217image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:29.568233image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:31.473212image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:33.414531image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:35.326523image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:37.186224image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-06-28T16:04:39.165603image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-06-28T16:04:49.802840image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
acousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessspeechinesstempovalencepopularitymodetime_signature
acousticness1.000-0.234-0.057-0.6560.121-0.016-0.112-0.508-0.172-0.230-0.116-0.0790.0720.134
danceability-0.2341.000-0.1180.191-0.2870.018-0.1260.2850.3220.0530.4440.0950.0660.289
duration_ms-0.057-0.1181.0000.085-0.0160.006-0.0520.109-0.1680.025-0.1390.0610.0190.019
energy-0.6560.1910.0851.000-0.2220.0390.1890.7600.2260.2150.3010.1090.0770.143
instrumentalness0.121-0.287-0.016-0.2221.000-0.022-0.075-0.453-0.286-0.065-0.281-0.2040.0100.067
key-0.0160.0180.0060.039-0.0221.0000.0090.0250.0240.0030.0420.0020.2720.022
liveness-0.112-0.126-0.0520.189-0.0750.0091.0000.0850.0780.0150.000-0.0130.0150.038
loudness-0.5080.2850.1090.760-0.4530.0250.0851.0000.1470.1880.3160.2570.0380.162
speechiness-0.1720.322-0.1680.226-0.2860.0240.0780.1471.0000.1070.1640.0160.0690.067
tempo-0.2300.0530.0250.215-0.0650.0030.0150.1880.1071.0000.0850.0300.0230.499
valence-0.1160.444-0.1390.301-0.2810.0420.0000.3160.1640.0851.0000.0140.0360.111
popularity-0.0790.0950.0610.109-0.2040.002-0.0130.2570.0160.0300.0141.0000.0180.051
mode0.0720.0660.0190.0770.0100.2720.0150.0380.0690.0230.0360.0181.0000.051
time_signature0.1340.2890.0190.1430.0670.0220.0380.1620.0670.4990.1110.0510.0511.000

Missing values

2023-06-28T16:04:41.171036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-06-28T16:04:41.570046image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

artist_nametrack_idtrack_nameacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencepopularity
0YG2RM4jf1Xa9zPgMGRDiht8OBig Bank feat. 2 Chainz, Big Sean, Nicki Minaj0.0058200.7432383730.3390.00010.0812-7.67810.4090203.92740.11815
1YG1tHDG53xJNGsItRA3vfVgsBAND DRUM (feat. A$AP Rocky)0.0244000.8462148000.5570.00080.2860-7.25910.4570159.00940.3710
2R3HAB6Wosx2euFPMT14UXiWudMyRadio Silence0.0250000.6031389130.7230.00090.0824-5.89000.0454114.96640.38256
3Chris Cooq3J2Jpw61sO7l6Hc7qdYV91Lactose0.0294000.8001253810.5790.91250.0994-12.11800.0701123.00340.6410
4Chris Cooq2jbYvQCyPgX3CdmAzeVeuSSame - Original mix0.0000350.7831240160.7920.87870.0332-10.27710.0661120.04740.9280
5Curbo26Y1lX7ZOpw9Ql3gGAlqLKDebauchery - Original mix0.0011500.8101240160.4170.91990.1060-10.78300.0793120.02540.8370
6Bingo Play5eIyK73BrxHLnly4F9PWqgGrandma - Original mix0.0005390.8191327420.7200.86340.0727-8.89500.1510124.00340.9340
7G Herbo13Mf2ZBpfNkgWJowvM5hXhBon appétit0.1150000.8851818380.3480.00090.1070-12.56910.4510142.11140.1800
834 Feet7BQaRTHk44DkMhIVNcXy2DAmong - Original mix0.0000580.7401240160.4720.84780.0959-9.00810.0551120.03440.6220
9Chris Cooq049RxG2laEl9U1PGYeIqLVHazard - Original mix0.0000810.8131327420.7310.910110.0727-8.93210.0697124.03140.9440
artist_nametrack_idtrack_nameacousticnessdanceabilityduration_msenergyinstrumentalnesskeylivenessloudnessmodespeechinesstempotime_signaturevalencepopularity
130653Dave East6oehXbAxVzX6tqepCk7oOaNo Pork0.108000.6781963640.7950.000000100.0995-6.12600.3440176.15740.924049
130654Gregory Alan Isakov0gcN4mJqOgwDl9az069JupPowder0.739000.4981922270.3930.77100040.1140-10.60000.0289112.98640.118048
130655Atreyu1RAT7pGtFKGsVe38SDKvKoAnger Left Behind0.056700.4162035470.9870.00014500.0853-3.33310.2100155.04540.258054
130656Echos6Tgd0DhWYzCWrwZZB6BPNwRevival0.668000.2112790700.2840.00011110.1050-10.31710.038583.90540.063851
130657City Girls7224Tp60srKUp78CkPfAo9Drip0.181000.8801525440.6270.000000110.1020-5.42900.2670151.99540.393048
130658Calum Scott0cvfSKcm9VeduwyYPrxtLxCome Back Home0.006780.6011905390.8010.000000110.0991-5.17410.0323131.04940.289057
130659Saint Claire43MP9F7UzvfilSrw2SqZGJEnough for You0.918000.3871945830.2490.00000090.1030-13.23310.043794.03940.346060
130660Mike Stud4TWlUuFk81NGUNKwndyS5QDo It0.330000.7171391910.5320.00000080.0997-8.35100.2060156.97740.546047
130661D Savage5iGBXzOoRo4sBTy8wdzMyKNo Smoke0.007900.7721800130.5100.00000040.1310-9.67000.1200120.04940.075550
130662Banda Los Sebastianes7LNtyuekYHiZ99UxkrfCQREn Vida0.549000.7151458270.7340.00000030.1080-3.24410.0367130.12830.976055